Dataset Information

Structural MRI at 7T reveals amygdala nuclei and hippocampal subfield volumetric association with Major Depressive Disorder symptom severity.

ABSTRACT: Subcortical volumetric changes in major depressive disorder (MDD) have been purported to underlie depressive symptomology, however, the evidence to date remains inconsistent. Here, we investigated limbic volumes in MDD, utilizing high-resolution structural images to allow segmentation of the hippocampus and amygdala into their constituent substructures. Twenty-four MDD patients and twenty matched controls underwent structural MRI at 7T field strength. All participants completed the Montgomery-Asberg Depression Rating Scale (MADRS) to quantify depressive symptomology. For the MDD group, volumes of the amygdala right lateral nucleus (p?=?0.05, r²?=?0.24), left cortical nucleus (p?=?0.032, r²?=?0.35), left accessory basal nucleus (p?=?0.04, r²?=?0.28) and bilateral corticoamygdaloid transition area (right hemisphere p?=?0.032, r²?=?0.38, left hemisphere p?=?0.032, r²?=?0.35) each displayed significant negative associations with MDD severity. The bilateral centrocortical (right hemisphere p?=?0.032, r²?=?0.31, left hemisphere p?=?0.032, r²?=?0.32) and right basolateral complexes (p?=?0.05, r²?=?0.24) also displayed significant negative relationships with depressive symptoms. Using high-field strength MRI, we report the novel finding that MDD severity is consistently negatively associated with amygdala nuclei, linking volumetric reductions with worsening depressive symptoms.

SUBMITTER: Brown SSG

PROVIDER: S-EPMC6629636 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Structural MRI at 7T reveals amygdala nuclei and hippocampal subfield volumetric association with Major Depressive Disorder symptom severity.

Brown S S G SSG Rutland J W JW Verma G G Feldman R E RE Alper J J Schneider M M Delman B N BN Murrough J M JM Balchandani P P

Scientific reports 20190715 1

Subcortical volumetric changes in major depressive disorder (MDD) have been purported to underlie depressive symptomology, however, the evidence to date remains inconsistent. Here, we investigated limbic volumes in MDD, utilizing high-resolution structural images to allow segmentation of the hippocampus and amygdala into their constituent substructures. Twenty-four MDD patients and twenty matched controls underwent structural MRI at 7T field strength. All participants completed the Montgomery-As ...[more]

PMID: 31308432

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Project description:PurposeIdentifying relationships between clinical features and quantitative characteristics of the amygdala-hippocampal and thalamic subregions in mesial temporal lobe epilepsy (mTLE) may offer insights into pathophysiology and the basis for imaging prognostic markers of treatment outcome. Our aim was to ascertain different patterns of atrophy or hypertrophy in mesial temporal sclerosis (MTS) patients and their associations with post-surgical seizure outcomes. To assess this aim, this study is designed in 2 folds: (1) hemispheric changes within MTS group and (2) association with postsurgical seizure outcomes.Methods and materials27 mTLE subjects with mesial temporal sclerosis (MTS) were scanned for conventional 3D T1w MPRAGE images and T2w scans. With respect to 12 months post-surgical seizure outcomes, 15 subjects reported being seizure free (SF) and 12 reported continued seizures. Quantitative automated segmentation and cortical parcellation were performed using Freesurfer. Automatic labeling and volume estimation of hippocampal subfields, amygdala, and thalamic subnuclei were also performed. The volume ratio (VR) for each label was computed and compared between (1) between contralateral and ipsilateral MTS using Wilcoxon rank-sum test and (2) SF and not seizure free (NSF) groups using linear regression analysis. False Discovery rate (FDR) with significant level of 0.05 were used in both analyses to correct for multiple comparisons.ResultsAmygdala: The medial nucleus of the amygdala was the most significantly reduced in patients with continued seizures when compared to patients who remained seizure free. Hippocampus: Comparison of ipsilateral and contralateral volumes with seizure outcomes showed volume loss was most evident in the mesial hippocampal regions such as CA4 and hippocampal fissure. Volume loss was also most explicit in the presubiculum body in patients with continued seizures at the time of their follow-up. Ipsilateral MTS compared to contralateral MTS analysis showed the heads of the ipsilateral subiculum, presubiculum, parasubiculum, dentate gyrus, CA4, and CA3 were more significantly affected than their respective bodies. Volume loss was most noted in mesial hippocampal regions. Thalamus: VPL and PuL were the most significantly reduced thalamic nuclei in NSF patients. In all statistically significant areas, volume reduction was observed in the NSF group. No significant volume reductions were noted in the thalamus and amygdala when comparing ipsilateral to contralateral sides in mTLE subjects.ConclusionsVarying degrees of volume loss were demonstrated in the hippocampus, thalamus, and amygdala subregions of MTS, especially between patients who remained seizure-free and those who did not. The results obtained can be used to further understand mTLE pathophysiology.Clinical relevance/applicationIn the future, we hope these results can be used to deepen the understanding of mTLE pathophysiology, leading to improved patient outcomes and treatments.

Project description:Accurate diagnosis of the initial phase of Alzheimer's disease (AD) is essential and crucial. The objective of this research was to employ efficient biomarkers for the diagnostic analysis and classification of AD based on combining structural MRI (sMRI) and resting-state functional MRI (rs-fMRI). So far, several anatomical MRI imaging markers for AD diagnosis have been identified. The use of cortical and subcortical volumes, the hippocampus, and amygdala volume, as well as genetic patterns, has proven to be beneficial in distinguishing patients with AD from the healthy population. The fMRI time series data have the potential for specific numerical information as well as dynamic temporal information. Voxel and graphical analyses have gained popularity for analyzing neurodegenerative diseases, such as Alzheimer's and its prodromal phase, mild cognitive impairment (MCI). So far, these approaches have been utilized separately for the diagnosis of AD. In recent studies, the classification of cases of MCI into those that are not converted for a certain period as stable MCI (MCIs) and those that converted to AD as MCIc has been less commonly reported with inconsistent results. In this study, we verified and validated the potency of a proposed diagnostic framework to identify AD and differentiate MCIs from MCIc by utilizing the efficient biomarkers obtained from sMRI, along with functional brain networks of the frequency range .01-.027 at the resting state and the voxel-based features. The latter mainly included default mode networks (amplitude of low-frequency fluctuation [ALFF], fractional ALFF [ALFF], and regional homogeneity [ReHo]), degree centrality (DC), and salience networks (SN). Pearson's correlation coefficient for measuring fMRI functional networks has proven to be an efficient means for disease diagnosis. We applied the graph theory to calculate nodal features (nodal degree [ND], nodal path length [NL], and between centrality [BC]) as a graphical feature and analyzed the connectivity link between different brain regions. We extracted three-dimensional (3D) patterns to calculate regional coherence and then implement a univariate statistical t-test to access a 3D mask that preserves voxels showing significant changes. Similarly, from sMRI, we calculated the hippocampal subfield and amygdala nuclei volume using Freesurfer (version 6). Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. We also compared the performance of SVM with Random Forest (RF) classifiers. The obtained results demonstrated the potency of our framework, wherein a combination of the hippocampal subfield, the amygdala volume, and brain networks with multiple measures of rs-fMRI could significantly enhance the accuracy of other approaches in diagnosing AD. The accuracy obtained by the proposed method was reported for binary classification. More importantly, the classification results of the less commonly reported MCIs vs. MCIc improved significantly. However, this research involved only the AD Neuroimaging Initiative (ADNI) cohort to focus on the diagnosis of AD advancement by integrating sMRI and fMRI. Hence, the study's primary disadvantage is its small sample size. In this case, the dataset we utilized did not fully reflect the whole population. As a result, we cannot guarantee that our findings will be applicable to other populations.

Dataset Information

Structural MRI at 7T reveals amygdala nuclei and hippocampal subfield volumetric association with Major Depressive Disorder symptom severity.

Publications

Structural MRI at 7T reveals amygdala nuclei and hippocampal subfield volumetric association with Major Depressive Disorder symptom severity.

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